Gary G. Yen

Artificial Intelligence · IEEE Transactions on Evolutionary Computation · 2004–2020

A researcher in the Science corpus: 18 papers; trusts 872 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 384 citations into it; #4,060 of 35,345 by trust here, #7,010 by citations
• Computer Vision and Pattern Recognition — 222 citations into it; #9,449 of 21,575 by trust here, #12,992 by citations
• Computational Theory and Mathematics — 155 citations into it; #841 of 12,182 by trust here, #1,529 by citations
• Model Architectures — 132 citations into it
• Optimization & Training — 65 citations into it
• Neural Architecture Search & AutoML — 59 citations into it
• Management Science and Operations Research — 41 citations into it; #6,414 of 11,946 by trust here, #7,638 by citations
• Evaluation & Benchmarks — 34 citations into it

Whom Gary G. Yen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kalyanmoy Deb
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. L. Eon Bottou
9. Eckart Zitzler
10. Karen Simonyan

Who trusts Gary G. Yen overall

1. Yanan Sun
2. Bing Xue
3. Mengjie Zhang
4. Tetsuyuki Takahama
5. Yuqiao Liu
6. Yong Wang
7. Efrén Mezura-Montes
8. Ye Tian
9. Rammohan Mallipeddi
10. Zixing Cai

Papers from Gary G. Yen's perspective

Ordered by how strongly the researchers Gary G. Yen trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Constraint Handling in Multiobjective Evolutionary Optimization (2009) — 383 citations
• A generic framework for constrained optimization using genetic algorithms (2005) — 338 citations
• Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms (2014) — 319 citations
• A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019) — 110 citations
• Reference line-based Estimation of Distribution Algorithm for many-objective optimization (2017) — 25 citations
• Improved Regularity Model-Based EDA for Many-Objective Optimization (2018) — 43 citations
• Problems with fitting to the power-law distribution (2004) — 693 citations
• IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018) — 472 citations
• Transfer Learning-Based Dynamic Multiobjective Optimization Algorithms (2016) — 346 citations
• A Survey on Evolutionary Neural Architecture Search (2020) — 590 citations
• Completely Automated CNN Architecture Design Based on Blocks (2020) — 301 citations
• Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020) — 266 citations
• Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation (2020) — 59 citations
• Particle swarm optimization of deep neural networks architectures for image classification (2019) — 343 citations
• Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification (2018) — 29 citations
• Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (2018) — 729 citations
• Evolving Deep Convolutional Neural Networks for Image Classification (2017) — 659 citations
• Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations (2017) — 139 citations